Exploring the Feasibility of Employing a Hybrid Machine Learning Method to Unpack Student Reasoning Patterns in Physics Essays
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917982876205056 |
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| author | Allen, Winter Rebello, N. Sanjay |
| author_facet | Allen, Winter Rebello, N. Sanjay |
| contents | We propose a novel clustering pipeline that combines two classic clustering algorithms to better understand student problem-solving strategies. This unsupervised machine learning method helps uncover patterns in reasoning without pre-defined labels. We applied it to essays written for an online multiple-choice quiz, the resulting clusters showed strong statistical alignment with students' selected answers. We also report on the resulting clusters of the hybrid pipeline compared to that of K-Means (MacQueen, 1967) and Hierarchal Density-Based Spatial Clustering of Application with Noise (HDBSCAN) (McInnes, Healy, and Astels, 2017) by analyzing the Scatter Plots, Silhouette Scores (Rousseeuw, 1987), and Davies Bouldin Index (Davies and Bouldin, 1979). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08904 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Feasibility of Employing a Hybrid Machine Learning Method to Unpack Student Reasoning Patterns in Physics Essays Allen, Winter Rebello, N. Sanjay Physics Education We propose a novel clustering pipeline that combines two classic clustering algorithms to better understand student problem-solving strategies. This unsupervised machine learning method helps uncover patterns in reasoning without pre-defined labels. We applied it to essays written for an online multiple-choice quiz, the resulting clusters showed strong statistical alignment with students' selected answers. We also report on the resulting clusters of the hybrid pipeline compared to that of K-Means (MacQueen, 1967) and Hierarchal Density-Based Spatial Clustering of Application with Noise (HDBSCAN) (McInnes, Healy, and Astels, 2017) by analyzing the Scatter Plots, Silhouette Scores (Rousseeuw, 1987), and Davies Bouldin Index (Davies and Bouldin, 1979). |
| title | Exploring the Feasibility of Employing a Hybrid Machine Learning Method to Unpack Student Reasoning Patterns in Physics Essays |
| topic | Physics Education |
| url | https://arxiv.org/abs/2504.08904 |